Techniq es in Wate shed Management”

  M odu le 6 – ( L2 2 – L2 6 ) : “ Use of M ode r n Te ch n iqu e s in W a t e r sh e d M a n a ge m e n t ” Te ch n iq e s in W a t e sh e d M a n a ge m e n t ” Applica t ion s of Ge ogr a ph ica l I n for m a t ion Syst e m a n d Re m ot e Se n sin g in W a t e r sh e d M a n a ge m e n t , Role of g g , D e cision Su ppor t Syst e m in W a t e r sh e d M a n a ge m e n t Applica t ion s of Kn ow le dge 2 6 2 6 B Ba se d M ode ls in W a t e r sh e d d M d l i W t h d

  Applications of Knowledge Based L26– L26 Models in Watershed Management d l h d

  

Topics Cove r e d Topics Cove r e d

    Knowledge based Modeling, Multi criteria Knowledge based Modeling, Multi criteria decision analysis, Fuzzy Logic based decision analysis Fuzzy Logic based decision analysis, Fuzzy Logic based decision analysis Fuzzy Logic based modeling, Fuzzy systems, Applications of modeling, Fuzzy systems, Applications of Knowledge based Systems in Watershed Knowledge based Systems in Watershed K K l d l d b b d S d S i i W W h d h d Management. Management.

  Knowledge based model; Multi criteria Knowledge based model; Multi criteria  

  Keywords: Keywords: decision analysis; Fuzzy logic based modeling decision analysis; Fuzzy logic based modeling decision analysis; Fuzzy logic based modeling decision analysis; Fuzzy logic based modeling .. .. Knowledge Based Model (KBM) Knowledge Based Model (KBM) 

  Knowledge-based systems are computer systems that

  are programmed to imitate the human problem are programmed to imitate the human problem- solving ability by means of artificial intelligence (AI) tools.

   Human reasoning Human reasoning using natural language can be using natural language can be

  reproduced in knowledge-based systems through AI tools.

  

In KBM , knowledge can be: knowledge derived from

  basic analysis, experts’ knowledge collected through surveys, & heuristic information from the field. surveys, & heuristic information from the field.

   Experts’ knowledge and heuristic information related

  to the specific problems are generally stored in the form of a rule-base. form of a rule base Knowledge Based Modeling Knowledge Based Modeling 

  A knowledge base is an organized body of knowledge that provides a formal logical specification for the that provides a formal logical specification for the interpretation of information

  

  In the knowledge-based modeling approach , watershed assessment is a watershed assessment is a multi-criteria multi criteria evaluation in evaluation in which knowledge of the experts is used to define the factors characterizing the watershed and the logic relations between the factors. l b h f

  

  The knowledge base encapsulates the assessment criteria and the relationships in an explicit form so criteria and the relationships in an explicit form so that they can be easily examined, modified, or updated.

Kn ow le dge Ba se d Syst e m s

  

  The knowledge base contains knowledge & experience for the subject domain (domain knowledge) & specifies logical relations among topics of interest to an logical relations among topics of interest to an assessment.

   Inference engine performs knowledge-based

  approximate reasoning to draw conclusions about the approximate reasoning to draw conclusions about the state of the system.

  

  By integrating knowledge-based reasoning into a GIS environment provide decision support for watershed management.

    GIS application provides GIS application provides database management, spatial database management spatial analysis, system interface, and map display.

   Assessment system allows user to evaluate knowledge

  b base for a specific spatial database & view the results. f ifi i l d b & i h l

Kn ow le dge Ba se St r u ct u r e

   Knowledge base structure is a hierarchy of

  dependency networks. Each network evaluates a dependency networks. Each network evaluates a specific proposition about the state of watershed condition.

   Knowledge base structure Knowledge base structure is designed to address the is designed to address the

  issues concerned by the watershed managers and to reflect their opinions on the importance of each issue.

  

  At the top of the

  hierarchy is the network wat ershed

  for the proposition that the overall condition

  condit ion

  of the watershed is suitable for sustaining healthy of the watershed is suitable for sustaining healthy populations of the native

  

  The wat ershed condit ion network depends on two o e e e e o lower level networks: st ream condit ion and upland s s ea co d o a d up a d

  condit ion Multi Criteria Decision Analysis (MCDA)  

  Simulation Models Simulation Models of various hydrologic components of of various hydrologic components of

  watershed - integrated with AI tools so as to make use of experts’ knowledge & heuristic information in decision making process; ki used to help d t h l the end users to arrive at the th d t i t th best suitable decisions related to irrigation management.

   Irrigation assessment & management g g - multi-criteria

  decision analysis ( MCDA ) problems - use knowledge-based systems.

    MCDA MCDA - in which the land suitability criteria, water in which the land suitability criteria water

  availability & irrigation requirement - various criteria to be evaluated- objective max. agricultural production.

   MCDA models -used in irrigation management to identify

  areas that can be irrigated, water release during different time period & best suitable cropping pattern for area p pp g p

  

Typical Knowledge Based Systems for WM

SCS-CN-based model Soil moisture balance Fuzzy membership approach Crop water requirement Water availability

  

Irrigation requirement Land suitability Water harvesting potential SMCDA for identifying the most suitable cropping zones SM CD A – Spa t io t e m por a l M u lt i Cr it e r ia D e cision An a lysis p p y Re f: Re sh m ide vi ( 2 0 0 8 ) , Ph D t h e sis, D e pt . Civil, I I T Bom ba y Fuzzy Logic Based System

  Lotfi Zadeh, first presented fuzzy logic in the mid-1960's, at

• the University of California at Berke

  Zadeh developed fuzzy logic as a way of processing data Zadeh developed fuzzy logic as a way of processing data later on he introduced idea of partial set membership

• Definition of fuzzy : “not clear, distinct, or precise”
• Definition of fuzzy logic

• - Fuzzy Logic (FL) is a multi-valued logic that allows

  intermediate values to be defined between conventional intermediate values to be defined between conventional evaluations like true/false, yes/no, high/low, etc.

  Fuzzy Fuzzy Probability ≠ Probability

  Probability deals with uncertainty and likelihood

• Fuzzy logic deals with ambiguity and vagueness
• Ref: L.A. Zadeh (1965) Fuzzy sets. Information and Control 8 (3) 338-353.

  A A B Why Fuzzy Logic?.

  C C 

  Based on intuition and judgment

  

  No need for a mathematical model

  

  Provides Provides a a smooth smooth transition transition between between members members and and nonmembers

  

  Relatively simple, fast and adaptive

  

  Less sensitive to system fluctuations

  

  Because of the rule based operation,

  

  C Can implement design objectives, difficult to express l d b d ff l mathematically, in linguistic or descriptive rules

   Conventional or crisp sets are binary. Conventional or crisp sets are binary. 

  An element either belongs to the set or doesn't. Example- -[ True, False] OR [ 0, 1] .

  Ref: D. Dubois & H. Prade (1988) Fuzzy Sets and Systems. Academic Press, New York. Fuzzy Sets 

  Allow elements to be partially in a set

  

  Each element is given a degree of membership in a set

  

  A membership function is the relationship between the A membership function is the relationship between the values of an element and its degree of membership in a set

  (N = Negative, P = Positive, L = Large, M = Medium, S = Small) Ref: D. Dubois & H. Prade (1988) Fuzzy Sets and Systems. Academic Press, New York. Ref: D. Dubois & H. Prade (1988) Fuzzy Sets and Systems. Academic Press, New York.

  . Membership Functions 

  Crisp membership functions 

  Crisp membership functions ( μ) are either one or zero

  

  Example- Number greater than 10

   A= { x / x > 1 0 } . 

  Fuzzy membership functions 

  Fuzzy membership functions 

  Membership value of not only 0 or 1

  

  The degree of truth of a statement can g range between 0 and 1

  

  Linguistic variables are used for fuzzy measures

  

  Examples of fuzzy measures include close, medium, heavy, light, big, small, smart, fast, slow, hot, cold, tall and short ^{Fuzzy measures} slow, hot, cold, tall and short Ref: D. Dubois & H. Prade (1988) Fuzzy Sets and Systems. Academic Press, New York. ^measures Fuzzy Logic Operations 

  Union

  

  Intersection

  

  Complement

   the negation of the specified membership specified membership function

  Ref: D Dubois & H Prade (1988) Fuzzy Sets and Systems Academic Press New York Ref: D. Dubois & H. Prade (1988) Fuzzy Sets and Systems. Academic Press, New York.

  Fuzzy Logic - Applications 

  Fuzzy logic concepts can be applied in: 

  Ride smoothness control

  

  Braking systems; High performance drives Braking systems; High performance drives

  

  Air-conditioning systems; Digital image processing

  

  Washing machines Washing machines

   Pattern recognition in remote sensing. 

  Video game artificial intelligence

   Graphics controllers for automated police sketchers. 

Watershed related applications: Rainfall-runoff processes.



  Erosive soil measurement;hydro-ecological modeling Erosive soil measurement;hydro ecological modeling over watershed; Flood forecasting; Water quality problems; Cropping & irrigation management Fuzzy Logic – Advantages & Limitations 

Adva n t a ge s

• Allows the use of vague linguistic terms in the rules
• No mathematical model
• Rule based and descriptive type

  

Lim it a t ion s

• Difficult to estimate membership function
• Difficult to estimate membership function
• There are many ways of interpreting fuzzy rules
• Combining the outputs of several fuzzy rules and defuzzifying the output
• Combining the outputs of several fuzzy rules and

Fuzzy System Fuzzy System – – Basic Components Basic Components Basic component

• Input 

  Fuzzification

• Fuzzy base rule
• Defuzzification

  Fuzzy rule Data input Data output _{Fu z z ifica t ion D e fu z z ifica t ion} Fuzzy output

• Output

Fu z z ifica t ion Fu z z ifica t ion

  

Fuzzifier converts a crisp input into a fuzzy variable

  

  It converts each piece of input data to degrees of membership

  

  The membership function is a graphical representation

  

  The membership function is a graphical representation of the magnitude of participation

  

  Definition of the membership functions must

• – reflects the designer's knowledge
• – provides smooth transition between member and nonmembers of a fuzzy set nonmembers of a fuzzy set

  

  Typical shapes of the membership function Gaussian; Trapezoidal; Triangular (commonly used) p g ( y )

Fu z z y Ba se Ru le

  

  Fuzzy based 0.2 0.4 0.6 0.8 1 Fuzzy based decision

  Eg: IF height is tall and weight is medium THEN healthy (H)

  Fun f U LH SH H

  Eg: IF height is tall and weight is

  

  Rule Function: F ={H,SH,LH,U}

  

  Include all possible fuzzy relations between inputs and outputs outputs

  

  Rules are tabulated as fuzzy words

  

  Rules reflect expert’s decisions

  

  Rules are expressed in the IF-THEN format

  

  Eg:– Healthy (H); Somewhat healthy (SH); Less Healthy (LH); Unhealthy (U) Less Healthy (LH); Unhealthy (U)

D e fu z z ifica t ion D e fu z z ifica t ion

  

  Defuzzification converts the resulting fuzzy outputs from the fuzzy inference engine to a number from the fuzzy inference engine to a number

  

  Converting the output fuzzy variable into a unique number f f h d

  

  Defuzzification Methods weighted average maximum membership maximum membership average maximum membership centre of gravity

  

Knowledge-Based Model Development

_ Re sh m ide vi ( 2 0 0 8 ) , Kn ow le dge ba se d M ode l for Su pple m e n t a r y I r r iga t ion Re sh m ide vi ( 2 0 0 8 ) , “Kn ow le dge - ba se d M ode l for Su pple m e n t a r y I r r iga t ion Asse ssm e n t in Agr icu lt u r a l W a t e r sh e ds” Ph D t h e sis, D e pt . Civil, I I T Bom ba y

   Fuzzy rule-based inference system for land suitability evaluation suitability evaluation

   SMCDA model for identifying the scope for supplementary irrigation supplementary irrigation

   Graphical user interface

  

  5 Steps in the Model Development

  5 Steps in the Model Development

• – Fuzzification of the attributes
• – Estimation of the intermediate land suitability index
• – Generation of the fuzzy rule base
• – Aggregation of the rules (Fuzzy output in terms of 5 suitability classes) suitability classes)
• – Defuzzification

Fuzzy Rule-Based Inference System 

  Problem with large number of attributes

  

  Problem with large number of attributes

  

  Hierarchical classification is adopted

  

  Considered both land potential and water potential Fuzzification 

  Attribute values are mapped into [0,1] 

  Two types of attributes: Thematic attributes for land potential  Unique membership value for each class; Continuously expressed attributes for land potential expressed attributes for land potential  Semantic import  Semantic import membership function; Asymmetric left (AL), Asymmetric right (AR) or Optimal range (OR)

  

  I I n t e r m e dia t e La n d Su it a bilit y I n de x t di t L d S it bilit I d 

  Weighted aggregation of the attribute membership values

  

  Att ib t i ht U i Attribute weights – Using Saaty’s relative importance scale S t ’ l ti i t l (Saaty, 1980)

   

  Relative importance is assumed based on literature, field Relative importance is assumed based on literature field observation and heuristic information

   

  Gives intermediate LSI in three suitability classes (Good, Gives intermediate LSI in three suitability classes (Good, Moderate and Not-suitable) based on land & water potential

  

Fuzzy Rule Base and Aggregation of the Rules



  Su it a bilit y cr it e r ia

• – Ex pr e sse d in t h e for m of I F..TH EN r u le s
• – I n t e r m s of in t e r m e dia t e su it a bilit y in dice s _{AND good is terrain good is potential water AND good is LU}
_IF

  AND moderate is terrain moderate is potential water AND good is LU ^{IF THEN excellent is area the AND good are parameters other Good is stics characteri chemical - physico AND AND good is terrain good is potential water AND good is LU IF } AND suitable is terrain suitable not is potential water AND suitable not is LU ^{IF THEN moderate is area the AND moderate are parameters other moderate is stics characteri chemical - physico AND } _{THEN suitable not is area the AND suitable not are parameters other suitable not is stics characteri chemical - physico AND}

   Generate fuzzy output in terms of 5 suitability classes (Excellent Good Moderate less suitable and Not suitable) (Excellent, Good, Moderate, less-suitable and Not-suitable)

   D e fu z z ifica t ion : Convert the fuzzy output into a single value (LSI

• )- Maximum centroid method
Generation of the Best Suitable Crop Map
•  

  Relative importance & LSI Relative importance & LSI 

  Three cases:

  Case 1- LSI * of existing crop < another crop of higher priority; higher priority crop is selected g p y g p y p

  

Case 2- LSI * of the existing crop < another crop of lesser priority

  Change in the cropping pattern if less suit able or not suit able for the existing crop

  Case 3- LSI * is same for more than one potential crop 

  If less suit able or not suit able for the existing crop, and if relative importance of the existing crop < the other crop,

• – A change in the cropping pattern is proposed A change in the cropping pattern is proposed
• – Replaces the existing crop with the higher priority one

  

SMCDA Model for Irrigation Feasibility Analysis

Irrigation requirement Runoff availability

Runoff Runoff

  

Vs

Water deficit periods

  

Irrigation requirement

Runoff

Vs Vs

Land suitability map L d it bilit

  Irrigable areas I i bl

Priority-based Irrigation requirement

  

Outsource water requirement

for different suitability classes for different suitability classes

  Ca se St u dy: Harzul Watershed

Reshmidevi (2008), “Knowledge-based Model for Supplementary Irrigation Assessment in Agricultural

Watersheds” PhD thesis, Dept. Civil, IIT Bombay

  Location- Nashik district, Maharashtra, India T i l h id li t Tropical humid climate

  

  Area: 10.9 sq. km P i i l

  Paddy and finger millet 

  Principle crops:

  Paddy and finger millet Reshmidevi, T.V., Eldh o T.I ., Jana, R., (2010 “Knowledge-Based Model for Supplementary Irrigation Assessment in Agricultural Watersheds” Jou r n a l of I r r iga t ion a n d D r a in a ge , ASCE, 2010, Vol. 136, No.6, pp. 376-382. Jou r n a l of I r r iga t ion a n d D r a in a ge , ASCE, 2010, Vol. 136, No.6, pp. 376 382. Generation of the Database Generation of the Database 

  H e u r ist ic in for m a t ion & fie ld obse r va t ion

• – At t r ibu t e s
• – At t r ibu t e su it a bilit y for diffe r e n t cr ops
• – Cr op pr ior it y & a gr icu lt u r a l pr a ct ice s
• – La n d su it a bilit y cr it e r ia

   M a p la ye r s

• D r a in a ge m a p D i
• La n d u se m a p>Con t ou r m a p
• D r a in a ge de n sit y m a p
• Soil m a p
• Pr ox im it y t o w a t e r body

  p p

• pH m a p

  P i it t t t l t

• Pr ox im it y t o se t t le m e n t
• M a ps sh ow in g spa t ia l va r ia t ion in EC, Sa lin it y e t c.

    H ydr o- m e t e or ologica l da t a H ydr o- m e t e or ologica l da t a

• Ra in fa ll
• Re la t ive h u m idit y>St r e a m flow
• Su n sh in e du r a t ion>Te m pe r a t u r e
• W in d spe e d

  Case St d Land S itabilit Case Study: Land Suitability Model6

₍

³⁰⁰ _{100 200}

_u

_n

_o

_ff

_mm)

_{Rainfall 100 200 a in fa ll ( m m ) Observed Q SimulatedQ 100}

  6/1 7/1 8/1 9/1 _Day

^R

^u

^{300 400 R Simulated Q a} Ye a r 1 9 9 7 – SCS- CN - SM S- SCS- CN m ode l in cor por a t in g Soil M oist u r e Sim u la t ion _{2002 3) 10 m 0.04 m 3)}

  Model 6 _{) 300} ⁴⁰⁰ _m ^{200 2002} _{150 m m)}

Rainfall hyetograph

²⁰⁰² ₂ ^{4 6 8 (M il li o n m} _0.01 ^{0.02 0.03 (M il li o n m} _{100 200}

  6/ ^{1 7/ 1 8/ 1 9/ 1} _10/ ^{M ( 1} _Day ^{m m 50 100 6/ 1 7/ 1 8/ 1 9/ 1} _10/ ^{R 1} _Day ^{a in fa ll ( m 6/ 1 7/ 1 8/ 1 9/ 1 10/ 1 Day} Irrigation requirement Accumulated runoff _Day

  Year 2002, Dry year ^Paddy field ^Day g q ccu u ated u o

Irrigation requirement in the Harsul watershed Irrigation requirement in the Harsul watershed

  2001

2002

  2003 2003 2004 Be st su it a ble cr oppin g z on e s Land Suitability for Crops in Harsul Watershed La n d su it a bilit y for pa ddy La n d su it a bilit y for fin ge r m ille t Suitability class Range of LSI

• ^{* % Area Suitability class Range of} _{membership values} ^{% Area}

  La n d su it a bilit y for pa ddy La n d su it a bilit y for fin ge r m ille t Not suitable 0 - 30 ⁸⁵ _{Less suitable 30 - 45 2 Moderately suitable 45 - 60 7} ^{Not suitable 0 - 30} _{Moderately suitable 45 - 60} ⁷⁸ _{Less suitable 30 - 45 1} Good 60 - 80 ⁶ _{Excellent 80 - 100} ^{Good 60 - 80 15} _{Excellent 80 - 100 6} Knowledge Based Modeling–Concluding Remarks 

  

Many decision-making and problem-solving tasks are too

easy to solve in the recent days using knowledge based

system & Fuzzy Logic. t & F L i

  

Fuzzy logic provides an alternative way to represent

linguistic and subjective attributes of the real world in

computing computing

   It is able to be applied to control systems and other

applications in order to improve the efficiency and simplicity

of the design process of the design process

  

Design objectives difficult to express mathematically can be

incorporated in a fuzzy controller by linguistic rules.

    The knowledge based model shows the irrigation requirement The knowledge-based model shows the irrigation requirement

for the predicted rainfall- helps to choose / adopt appropriate

crops & irrigation management plan

  _ Re fe r e n ce s Re fe r e n ce s _ L.A. Zadeh (1965) Fuzzy sets. Information and Control 8 (3) 338353.

  G.J. Klir, Bo Yuan (Eds.) (1996) Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems. _ Selected Papers by Lotfi A. Zadeh. World Scientific, Singapore. Gokmen Tayfur, Serhan Ozdemir, Vijay P. Singh(2003) Advances in Water Resources G k T f S h O d i Vij P Si h(2003) Ad i W R _ 26 1249–1256. Chang NB, Weu GG, Chen YL.( 1997-99) A fuzzy multi-objective programming

approach for optimal management of the reservoir watershed. Eur J Oper Res (2-

_ 1),289–302.

  

Yu P-S, Yang T-C.( 2000) Fuzzy multi-objective function for rainfall runoff model

_{ } calibration. J Hydrol;238(1–2), 1–14. _

  D. Dubois and H. Prade (1988) Fuzzy Sets and Systems. Academic Press, New York. D Dubois and H Prade (1988) Fuzzy Sets and Systems Academic Press New York Reshmidevi, T.V., Eldh o T.I ., Jana, R., (2010 “Knowledge-Based Model for Supplementary Irrigation Assessment in Agricultural Watersheds” Jou r n a l of _{ R Re sh m ide vi ( 2 0 0 8 ) , “Kn ow le dge - ba se d M ode l for Su pple m e n t a r y I r r iga t ion h id i ( 2 0 0 8 ) “K l d b d M d l f S l t} I r r iga t ion a n d D r a in a ge , ASCE, 2010, Vol. 136, No.6, pp. 376-382.

  I i t i

_{ Sa a t y, T. L. ( 1 9 8 0 ) . Th e An a lyt ic H ie r a r ch y Pr oce ss, M cGr a w - H ill Pu blish in g}

Asse ssm e n t in Agr icu lt u r a l W a t e r sh e ds” Ph D t h e sis, D e pt . Civil, I I T Bom ba y Com pa n y, N e w Yor k .

Tu t or ia ls - Qu e st ion !.?

   Critically study the applications of

knowledge based systems for various water knowledge based systems for various water

resources management problems. Study various case studies available in literature (details can be obtained from Internet).

    Study the role of knowledge based modeling Study the role of knowledge based modeling y y g g g g

in Integrated Water Resources Management. in Integrated Water Resources Management

  Prof. T I Eldho, Department of Civil Engineering, IIT Bombay

Se lf Eva lu a t ion - Qu e st ion s!. Q

  

  Describe the features of typical knowledge based models. models

  

  Illustrate the requirements of knowledge based systems. systems.

  

  Describe typical knowledge based system for watershed management. g

  

  Illustrate the fuzzy logic operators used in typical fuzzy logic.

  

  What are the important components of a fuzzy systems.

  

Prof. T I Eldho, Department of Civil Engineering, IIT Bombay

Assign m e n t - Qu e st ion s?. g Q

   Describe the structure of a knowledge based system.

   What are the important features of Multi Criteria Decision Analysis (MCDA).

  

Ill Illustrate the features of fuzzy logic based t t th f t f f l i b d

systems.

    Describe applications, advantages & Describe applications advantages & limitations of Fuzzy Logic?.

   Illustrate a typical Knowledge based model for watershed management.

Un solve d Pr oble m !. Un solve d Pr oble m !

    Critically study a typical knowledge based Critically study a typical knowledge based model for the water and land management model for the water and land management model for the water and land management model for the water and land management in a watershed. in a watershed.

     

For your watershed area, study the scope of For your watershed area study the scope of For your watershed area study the scope of For your watershed area, study the scope of

development of knowledge based model development of knowledge based model considering rainfall, various crops, land use, considering rainfall, various crops, land use, g g , , p , p , , , land suitability, water requirement etc. land suitability, water requirement etc.

  Prof. T I Eldho, Department of Civil Engineering, IIT Bombay

  Dr. T. I. Eldho Dr. T. I. Eldho Professor, Professor, Department of Civil Engineering, Department of Civil Engineering, p p g g g g Indian Institute of Technology Bombay, Indian Institute of Technology Bombay, Mumbai, India, 400 076. Mumbai, India, 400 076. Email: Email: Email: Email: eldho@iitb.ac.in eldho@iitb.ac.in eldho@iitb.ac.in eldho@iitb.ac.in Phone: (022) – Phone: (022) – 25767339; Fax: 25767302 25767339; Fax: 25767302